A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples

Recently, incremental learning (IL) has been widely used in intelligent fault diagnosis of electronic machinery. Most of the typical IL methods have adopted the exemplar-replay strategy to retain the learned diagnostic knowledge. However, it is almost impossible to have infinite storage space to retain fault samples in practical industrial scenarios, which brings a significant challenge for actual industrial applications. To solve this issue, a novel Relationship-Aware Calibrated Prototypical Network (RACPN) is proposed for incremental fault diagnosis of electric motors, which retains learned diagnostic knowledge without requiring the storage of any fault samples from previous sessions. Firstly, a fault prototype calibration (FPC) method is employed to learn new diagnostic knowledge from new sessions. Secondly, a task-relationship representation (TRR), which stands for a method to represent the relationships between tasks, is utilized to enhance the maintenance of diagnostic knowledge across different sessions. Finally, a Gaussian Bayes classifier with Mahalanobis metric is adopted to enhance the inference reliability for classifying fault categories. Experiments conducted on two electrical motor datasets demonstrate the superiority and effectiveness of the proposed RACPN. The results validate that current signals as model input can achieve satisfactory diagnostic performance. The proposed RACPN is a promising tool for incremental fault diagnosis in electric motors.